Graph applications have been increasingly explored in recent years because of numerous structural applications in various industries. The graph mining problem has been studied in the context of a number of traditional data mining problems such as clustering, classification, and frequent pattern mining.
Existing approaches include pre-computing all-pairs' shortest paths and storing them for responses to queries. However, this is not a practical solution for large graphs, and can require significant storage. Further, such approaches may only assume value pre-stores and may not provide any idea of what the actual path might be in the underlying result. Pre-storage of actual paths requires resources which are several orders of magnitude greater.